Energy-Efficient Activity Recognition Framework using Wearable Accelerometers

Acceleration data for activity recognition typically are collected on batterypowered devices, leading to a trade-off between high-accuracy recognition and energy-efficient operation. We investigate this trade-off from a feature selection perspective, and propose an energy-efficient activity recognition framework with two key components: a detailed energy consumption model and a number of feature selection algorithms. We evaluate the model and the algorithms using Random Forest classifiers to quantify the recognition accuracy, and find that the multi-objective Particle Swarm Optimization algorithm achieves the best results for the task. The results show that by selecting appropriate groups of features, energy consumption for computation and data transmission is reduced by an order of magnitude compared with the raw-data approach, and that the framework presents a flexible selection of feature groups that allow the designer to choose an appropriate accuracy-energy trade-off for a specific target application.